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Install in OpenClaw
/install mars-clouds-clustering-parallel-processing
Description
Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores.
Usage Guidance
This is a safe, coherent how-to for using joblib. Before using it: ensure the runtime Python environment has joblib installed and compatible versions of your dependencies; be cautious with n_jobs (using all cores can make the host unresponsive); watch memory usage when sharing large data objects across workers; test with small workloads first. If you run user-provided or untrusted 'expensive_computation' code, run it in a sandboxed environment to avoid executing unsafe code.
Capability Analysis
Type: OpenClaw Skill
Name: mars-clouds-clustering-parallel-processing
Version: 0.1.0
The skill bundle provides standard documentation and code examples for using the joblib library to perform parallel processing in Python. The content in SKILL.md is purely educational, focusing on n_jobs parameters, grid search patterns, and performance tips, with no evidence of malicious intent, data exfiltration, or prompt injection.
Capability Assessment
Purpose & Capability
Name and description claim joblib-based parallel processing; the SKILL.md contains only joblib usage examples and related tips — no unrelated credentials, binaries, or surprising access are requested.
Instruction Scope
Instructions are limited to Python code examples (Parallel, delayed, parameter grid, shared data) and performance advice. They do not reference reading arbitrary host files, environment variables, network endpoints, or other system credentials.
Install Mechanism
There is no install spec (instruction-only), so nothing is downloaded or written to disk by the skill itself — consistent with a code snippet / how-to skill.
Credentials
No environment variables, credentials, or config paths are requested. This is proportional for a library usage guide; the only implicit requirement is that joblib (and any user code like expensive_computation) be available in the runtime Python environment.
Persistence & Privilege
Skill is not forced-always and does not request any persistent agent privileges or modifications to other skills; autonomous invocation is allowed by default but not combined with other risky behaviors here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install mars-clouds-clustering-parallel-processing - After installation, invoke the skill by name or use
/mars-clouds-clustering-parallel-processing - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Bulk publish from all-task-skills-dedup
Metadata
Frequently Asked Questions
What is parallel-processing?
Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores. It is an AI Agent Skill for Claude Code / OpenClaw, with 75 downloads so far.
How do I install parallel-processing?
Run "/install mars-clouds-clustering-parallel-processing" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is parallel-processing free?
Yes, parallel-processing is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does parallel-processing support?
parallel-processing is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created parallel-processing?
It is built and maintained by lnj22 (@lnj22); the current version is v0.1.0.
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